The objective of this application is to develop and implement statistical methods to analyze types of data that commonly arise from cohort studies of infectious diseases such as human immunodeficiency virus (HIV), tuberculosis (TB),and human papillomaviruses (HPV). Such studies often produce incomplete information about times of infection, because they must rely on periodic testing to detect infection rather than immediate symptoms, and tests may be subject to error. The goals of the study also include addressing several key questions about the epidemiology of HIV, TB, HPV, and the influence of HIV on TB and HPV. Specific areas to be addressed include: estimating how the incubation period of AIDS has changed over time and how it is influenced by age; estimating TB infection rates and risk factors for infection (including HIV status) while accounting for errors in testing; estimating rates of progression to active TB and how they are influenced by HIV coinfection and other covariates; estimating acquisition and persistence rates of HPV infection and the influence of HIV and other covariates while accounting for errors in testing; estimating rates of pre-cancerous HPV disease outcomes and the influence of covariates; and estimating sexual transmission rates of HIV and HPV and how they are influenced by covariates. These goals will be accomplished by extending previous work to allow for convenient modeling of general covariate effects and to account for errors in tests, and by applying the resulting methods to several data sets. Maximum (penalized) likelihood methods and hidden Markov methods will be used, and the methods' performance will be studied using simulations.